Traditional approaches for managing enterprise data revolve around a batch driven Extract Transform Load (ETL) process, a one size fits all approach for storage, and an application architecture that is tightly coupled to the underlying data infrastructure. The emergence of Big Data technologies have led to the creation of alternate instantiations of the traditional approach, one where the storage systems have moved from relational databases to NoSQL technologies like Hadoop Distributed File Systems (HDFS). In some cases, traditional approaches to data control in the context of Internet of Things (IoT) and other enterprise data settings have brought forth challenges due to content heterogeneity, requirements of scale, and robustness of ETL processes.